CSFL: Cooperative Security Aware Federated Learning Model Using The Blockchain

• Published in: Computer journal Vol. 67; no. 4; pp. 1298 - 1308
• Main Authors: Zhang, Jiaomei, Ye, Ayong, Chen, Jianwei, Zhang, Yuexin, Yang, Wenjie
• Format: Journal Article
• Language: English
• Published: Oxford University Press 21.04.2024
• Subjects: Privacy; Federated learning; Economic model; Security; Blockchain
• ISSN: 0010-4620; 1460-2067
• DOI: 10.1093/comjnl/bxad060

Federated learning (FL) is a focus of research in the area of privacy protection since it does not have the privacy issues that arise from data concentration. Although its emergence has attracted widespread attention from academia and industry, existing works on FL still face security challenges. FL can be considered as a cooperative-based task to achieve global model sharing. However, the model raises issues of cooperative security, such as free-riding and poisoning attacks. Therefore, we focus on the behavior of participants with strong cooperative relationships and build a Cooperative Security-aware Federated Learning model using blockchain. In addition, we propose a credit-based economic model including profit and punishment mechanisms to ensure fairness and security among participants. Furthermore, for data privacy, we develop a participation permission strategy to protect the privacy of participants through proxy re-encryption and homomorphic encryption. Finally, the simulation results of the real datasets show that the proposed scheme achieves a good performance in security and accuracy.